book

The Machine Learning Workshop - Second Edition

Name: The Machine Learning Workshop - Second Edition
ISBN: 9781839219061

by Hyatt Saleh, John Wesley Doyle, Akshat Gupta, Harshil Jain, Vikraman Karunanidhi, Subhojit Mukherjee, Madhav Pandya, Subhash Sundaravadivelu

July 2020

Intermediate to advanced

286 pages

5h 17m

English

Packt Publishing

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The Machine Learning Workshop
Second Edition
Preface
About the BookAudienceAbout the ChaptersConventionsCode PresentationSetting up Your EnvironmentInstalling Python on Windows and MacOSInstalling Python on LinuxInstalling pipInstalling LibrariesOpening a Jupyter NotebookAccessing the Code Files
1. Introduction to Scikit-Learn
IntroductionIntroduction to Machine LearningApplications of MLChoosing the Right ML AlgorithmScikit-LearnAdvantages of Scikit-LearnDisadvantages of Scikit-LearnOther FrameworksData RepresentationTables of DataFeatures and Target MatricesExercise 1.01: Loading a Sample Dataset and Creating the Features and Target MatricesActivity 1.01: Selecting a Target Feature and Creating a Target MatrixData PreprocessingMessy DataMissing ValuesOutliersExercise 1.02: Dealing with Messy DataDealing with Categorical FeaturesFeature EngineeringExercise 1.03: Applying Feature Engineering to Text DataRescaling DataExercise 1.04: Normalizing and Standardizing DataActivity 1.02: Pre-processing an Entire DatasetScikit-Learn APIHow Does It Work?EstimatorPredictorTransformerSupervised and Unsupervised LearningSupervised LearningUnsupervised LearningSummary
2. Unsupervised Learning – Real-Life Applications
IntroductionClusteringClustering TypesApplications of ClusteringExploring a Dataset – Wholesale Customers DatasetUnderstanding the DatasetData VisualizationLoading the Dataset Using pandasVisualization ToolsExercise 2.01: Plotting a Histogram of One Feature from the Circles DatasetActivity 2.01: Using Data Visualization to Aid the Pre-processing Processk-means AlgorithmUnderstanding the AlgorithmInitialization MethodsChoosing the Number of ClustersExercise 2.02: Importing and Training the k-means Algorithm over a DatasetActivity 2.02: Applying the k-means Algorithm to a DatasetMean-Shift AlgorithmUnderstanding the AlgorithmExercise 2.03: Importing and Training the Mean-Shift Algorithm over a DatasetActivity 2.03: Applying the Mean-Shift Algorithm to a DatasetDBSCAN AlgorithmUnderstanding the AlgorithmExercise 2.04: Importing and Training the DBSCAN Algorithm over a DatasetActivity 2.04: Applying the DBSCAN Algorithm to the DatasetEvaluating the Performance of ClustersAvailable Metrics in Scikit-LearnExercise 2.05: Evaluating the Silhouette Coefficient Score and Calinski–Harabasz IndexActivity 2.05: Measuring and Comparing the Performance of the AlgorithmsSummary
3. Supervised Learning – Key Steps
IntroductionSupervised Learning TasksModel Validation and TestingData PartitioningSplit RatioExercise 3.01: Performing a Data Partition on a Sample DatasetCross-ValidationExercise 3.02: Using Cross-Validation to Partition the Train Set into a Training and a Validation SetActivity 3.01: Data Partitioning on a Handwritten Digit DatasetEvaluation MetricsEvaluation Metrics for Classification TasksConfusion MatrixAccuracyPrecisionRecallExercise 3.03: Calculating Different Evaluation Metrics on a Classification TaskChoosing an Evaluation MetricEvaluation Metrics for Regression TasksExercise 3.04: Calculating Evaluation Metrics on a Regression TaskActivity 3.02: Evaluating the Performance of the Model Trained on a Handwritten DatasetError AnalysisBias, Variance, and Data MismatchExercise 3.05: Calculating the Error Rate on Different Sets of Data Activity 3.03: Performing Error Analysis on a Model Trained to Recognize Handwritten DigitsSummary
4. Supervised Learning Algorithms: Predicting Annual Income
IntroductionExploring the DatasetUnderstanding the DatasetThe Naïve Bayes AlgorithmHow Does the Naïve Bayes Algorithm Work?Exercise 4.01: Applying the Naïve Bayes Algorithm Activity 4.01: Training a Naïve Bayes Model for Our Census Income DatasetThe Decision Tree AlgorithmHow Does the Decision Tree Algorithm Work?Exercise 4.02: Applying the Decision Tree Algorithm Activity 4.02: Training a Decision Tree Model for Our Census Income DatasetThe Support Vector Machine AlgorithmHow Does the SVM Algorithm Work?Exercise 4.03: Applying the SVM AlgorithmActivity 4.03: Training an SVM Model for Our Census Income DatasetError AnalysisAccuracy, Precision, and RecallSummary
5. Supervised Learning – Key Steps
IntroductionArtificial Neural NetworksHow Do ANNs Work?Forward PropagationCost FunctionBackpropagationUpdating the Weights and BiasesUnderstanding the HyperparametersNumber of Hidden Layers and UnitsActivation FunctionsRegularizationBatch SizeLearning RateNumber of IterationsApplications of Neural NetworksLimitations of Neural NetworksApplying an Artificial Neural NetworkScikit-Learn's Multilayer PerceptronExercise 5.01: Applying the MLP Classifier ClassActivity 5.01: Training an MLP for Our Census Income DatasetPerformance AnalysisError AnalysisHyperparameter Fine-TuningModel ComparisonActivity 5.02: Comparing Different Models to Choose the Best Fit for the Census Income Data ProblemSummary
6. Building Your Own Program
IntroductionProgram DefinitionBuilding a Program – Key StagesPreparationCreationInteractionUnderstanding the DatasetActivity 6.01: Performing the Preparation and Creation Stages for the Bank Marketing DatasetSaving and Loading a Trained ModelSaving a ModelExercise 6.01: Saving a Trained Model Loading a ModelExercise 6.02: Loading a Saved Model Activity 6.02: Saving and Loading the Final Model for the Bank Marketing DatasetInteracting with a Trained ModelExercise 6.03: Creating a Class and a Channel to Interact with a Trained Model Activity 6.03: Allowing Interaction with the Bank Marketing Dataset ModelSummary
Appendix
1. Introduction to Scikit-LearnActivity 1.01: Selecting a Target Feature and Creating a Target MatrixActivity 1.02: Pre-processing an Entire Dataset2. Unsupervised Learning – Real-Life ApplicationsActivity 2.01: Using Data Visualization to Aid the Pre-processing ProcessActivity 2.02: Applying the k-means Algorithm to a DatasetActivity 2.03: Applying the Mean-Shift Algorithm to a DatasetActivity 2.04: Applying the DBSCAN Algorithm to the DatasetActivity 2.05: Measuring and Comparing the Performance of the Algorithms3. Supervised Learning – Key StepsActivity 3.01: Data Partitioning on a Handwritten Digit DatasetActivity 3.02: Evaluating the Performance of the Model Trained on a Handwritten DatasetActivity 3.03: Performing Error Analysis on a Model Trained to Recognize Handwritten Digits4. Supervised Learning Algorithms: Predicting Annual IncomeActivity 4.01: Training a Naïve Bayes Model for Our Census Income DatasetActivity 4.02: Training a Decision Tree Model for Our Census Income DatasetActivity 4.03: Training an SVM Model for Our Census Income Dataset5. Artificial Neural Networks: Predicting Annual IncomeActivity 5.01: Training an MLP for Our Census Income DatasetActivity 5.02: Comparing Different Models to Choose the Best Fit for the Census Income Data Problem6. Building Your Own ProgramActivity 6.01: Performing the Preparation and Creation Stages for the Bank Marketing DatasetActivity 6.02: Saving and Loading the Final Model for the Bank Marketing DatasetActivity 6.03: Allowing Interaction with the Bank Marketing Dataset Model

Content preview from The Machine Learning Workshop - Second Edition

Appendix

1. Introduction to Scikit-Learn

Activity 1.01: Selecting a Target Feature and Creating a Target Matrix

Solution:

Load the titanic dataset using the seaborn library:
import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.head(10)
The first couple of rows should look as follows:
Figure 1.22: An image showing the first 10 instances of the Titanic dataset
Select your preferred target feature for the goal of this activity.
The preferred target feature could be either survived or alive. This is mainly because both of them label whether a person survived the crash. For the following steps, the variable that's been chosen is survived ...

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